资金流入流出预测-挑战Baseline EDA(1)

  1. 数据准备
  2. 时间序列分析
  3. 翌日特征分析
  4. 月份特诊分析
  5. 日期特征分析
  6. 节假日分析
  7. 节假日周围日期分析
  8. 异常值分析
  9. 用户交易纪录表中其他变量相关分析
  10. 银行、支付宝利率分析
  11. 小结

1. 数据准备

In [1]:
import pandas as  pd
import numpy as np
import warnings 
import datetime
import seaborn as sns
import matplotlib.pyplot as plt
import datetime 
from scipy import stats

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
sns.set_style('darkgrid')
In [2]:
import warnings
warnings.filterwarnings('ignore')
In [3]:
# 设置数据集路径

dataset_path = 'Dataset/'
In [4]:
# 读取数据

data_balance = pd.read_csv(dataset_path+'user_balance_table.csv')
In [5]:
# 为数据集添加时间戳

data_balance['date'] = pd.to_datetime(data_balance['report_date'], format= "%Y%m%d")
data_balance['day'] = data_balance['date'].dt.day
data_balance['month'] = data_balance['date'].dt.month
data_balance['year'] = data_balance['date'].dt.year
data_balance['week'] = data_balance['date'].dt.week
data_balance['weekday'] = data_balance['date'].dt.weekday
In [6]:
data_balance.head()
Out[6]:
user_id report_date tBalance yBalance total_purchase_amt direct_purchase_amt purchase_bal_amt purchase_bank_amt total_redeem_amt consume_amt ... category1 category2 category3 category4 date day month year week weekday
0 1 20140805 20385 20383 2 0 0 0 0 0 ... NaN NaN NaN NaN 2014-08-05 5 8 2014 32 1
1 1 20140808 20391 20389 2 0 0 0 0 0 ... NaN NaN NaN NaN 2014-08-08 8 8 2014 32 4
2 1 20140811 20397 20395 2 0 0 0 0 0 ... NaN NaN NaN NaN 2014-08-11 11 8 2014 33 0
3 1 20140814 20403 20401 2 0 0 0 0 0 ... NaN NaN NaN NaN 2014-08-14 14 8 2014 33 3
4 1 20140817 20409 20407 2 0 0 0 0 0 ... NaN NaN NaN NaN 2014-08-17 17 8 2014 33 6

5 rows × 24 columns

2. 时间序列分析

In [7]:
# 聚合时间数据
total_balance = data_balance.groupby(['date'])['total_purchase_amt','total_redeem_amt'].sum().reset_index()

In [8]:
# 生成测试集区段数据(时间区段为2014/9/1之后)

start = datetime.datetime(2014,9,1)
testdata = []
while start != datetime.datetime(2014,10,1):
    temp = [start, np.nan, np.nan]
    testdata.append(temp)
    start += datetime.timedelta(days = 1)
testdata = pd.DataFrame(testdata)
testdata.columns = total_balance.columns
In [9]:
# 拼接数据集

total_balance = pd.concat([total_balance, testdata], axis = 0)
In [10]:
# 为数据集添加时间戳

total_balance['day'] = total_balance['date'].dt.day
total_balance['month'] = total_balance['date'].dt.month
total_balance['year'] = total_balance['date'].dt.year
total_balance['week'] = total_balance['date'].dt.week
total_balance['weekday'] = total_balance['date'].dt.weekday
In [11]:
import matplotlib.pylab as plt

In [12]:
# 画出每日总购买与赎回量的时间序列图

fig = plt.figure(figsize=(20,6))
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],label='purchase')
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],label='redeem')

plt.legend(loc='best')
plt.title("The lineplot of total amount of Purchase and Redeem from July.13 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
In [13]:
# 画出4月份以后的时间序列图

total_balance_1 = total_balance[total_balance['date'] >= np.datetime64(datetime.date(2014,4,1))]
fig = plt.figure(figsize=(20,6))
plt.plot(total_balance_1['date'], total_balance_1['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_1['date'], total_balance_1['total_redeem_amt'], label = 'redeem')
plt.legend(loc = 'best')
plt.title("The lineplot of total amount of Purchase and Redeem from April.14 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
In [14]:
# 分别画出每个月中每天购买赎回量的时间序列图

fig = plt.figure(figsize=(15,15))

plt.subplot(4,1,1)
plt.title("The time series of total amount of Purchase and Redeem for August, July, June, May respectively")

total_balance_2 = total_balance[total_balance['date'] >= np.datetime64(datetime.date(2014,8,1))]
plt.plot(total_balance_2['date'], total_balance_2['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_2['date'], total_balance_2['total_redeem_amt'], label = 'redeem')
plt.legend()


total_balance_3 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,7,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,8,1)))]
plt.subplot(4,1,2)
plt.plot(total_balance_3['date'], total_balance_3['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_3['date'], total_balance_3['total_redeem_amt'], label = 'redeem')
plt.legend()


total_balance_4 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,6,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,7,1)))]
plt.subplot(4,1,3)
plt.plot(total_balance_4['date'], total_balance_4['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_4['date'], total_balance_4['total_redeem_amt'], label = 'redeem')
plt.legend()


total_balance_5 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,5,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,6,1)))]
plt.subplot(4,1,4)
plt.plot(total_balance_5['date'], total_balance_5['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_5['date'], total_balance_5['total_redeem_amt'], label = 'redeem')
plt.legend()

plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
In [15]:
# 分别画出13年8月与9月每日购买赎回量的时序图

fig = plt.figure(figsize=(15,9))

total_balance_last8 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,8,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,9,1)))]
plt.subplot(2,1,1)
plt.plot(total_balance_last8['date'], total_balance_last8['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_last8['date'], total_balance_last8['total_redeem_amt'], label = 'redeem')
plt.legend()

total_balance_last9 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,9,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,10,1)))]
plt.subplot(2,1,2)
plt.plot(total_balance_last9['date'], total_balance_last9['total_purchase_amt'], label = 'purchase')
plt.plot(total_balance_last9['date'], total_balance_last9['total_redeem_amt'], label = 'redeem')
plt.legend()

plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()

3. 翌日特征分析

In [16]:
# 画出每个翌日的数据分布于整体数据的分布图

plt.figure(figsize=(15,10))
scatter_para = {'marker':'.', 's':3, 'alpha':0.3}
line_kws = {'color':'k'}
plt.subplot(2,2,1)
plt.title('The distrubution of total purchase')
sns.violinplot(x='weekday', y='total_purchase_amt', data = total_balance_1, scatter_kws=scatter_para, line_kws=line_kws)
plt.subplot(2,2,2)
plt.title('The distrubution of total purchase')
sns.distplot(total_balance_1['total_purchase_amt'].dropna())
plt.subplot(2,2,3)
plt.title('The distrubution of total redeem')
sns.violinplot(x='weekday', y='total_redeem_amt', data = total_balance_1, scatter_kws=scatter_para, line_kws=line_kws)
plt.subplot(2,2,4)
plt.title('The distrubution of total redeem')
sns.distplot(total_balance_1['total_redeem_amt'].dropna())
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x27ba0bfa6c8>
In [17]:
# 按翌日对数据聚合后取均值

week_sta = total_balance_1[['total_purchase_amt', 'total_redeem_amt', 'weekday']].groupby('weekday', as_index=False).mean()

In [18]:
# 分析翌日的中位数特征

plt.figure(figsize=(12, 5))
ax = plt.subplot(1,2,1)
plt.title('The barplot of average total purchase with each weekday')
ax = sns.barplot(x="weekday", y="total_purchase_amt", data=week_sta, label='Purchase')
ax.legend()
ax = plt.subplot(1,2,2)
plt.title('The barplot of average total redeem with each weekday')
ax = sns.barplot(x="weekday", y="total_redeem_amt", data=week_sta, label='Redeem')
ax.legend()
Out[18]:
<matplotlib.legend.Legend at 0x27ba065b788>

In [19]:
# 画出翌日的箱型图 （箱形图最大的优点就是不受异常值的影响，可以以一种相对稳定的方式描述数据的离散分布情况。）

plt.figure(figsize=(12, 5))
ax = plt.subplot(1,2,1)
plt.title('The boxplot of total purchase with each weekday')
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=total_balance_1,palette = 'RdBu')
ax = plt.subplot(1,2,2)
plt.title('The boxplot of total redeem with each weekday')
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=total_balance_1, palette = 'RdBu')
In [20]:
# 使用OneHot方法将翌日特征划分，获取划分后特征

from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
total_balance = total_balance.reset_index()
week_feature = encoder.fit_transform(total_balance['weekday'].ravel().reshape(-1, 1)).toarray() 
# list to array --> https://zhuanlan.zhihu.com/p/261636002
# reshape是在不改变数据内容的情况下，改变一个数组的格式，参数及返回值，reshape(-1,1)令计算机计算出展开所有的行数，然后转换。
week_feature = pd.DataFrame(week_feature,columns=['weekday_onehot']*len(week_feature[0])) # 字符串复制
feature = pd.concat([total_balance, week_feature], axis = 1)[['total_purchase_amt', 'total_redeem_amt','weekday_onehot','date']]
feature.columns = list(feature.columns[0:2]) + [x+str(i) for i,x in enumerate(feature.columns[2:-1])] + ['date']
In [21]:
feature.head()
Out[21]:
total_purchase_amt total_redeem_amt weekday_onehot0 weekday_onehot1 weekday_onehot2 weekday_onehot3 weekday_onehot4 weekday_onehot5 weekday_onehot6 date
0 32488348.0 5525022.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2013-07-01
1 29037390.0 2554548.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2013-07-02
2 27270770.0 5953867.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2013-07-03
3 18321185.0 6410729.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2013-07-04
4 11648749.0 2763587.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 2013-07-05
In [22]:
# 画出划分后翌日特征与标签的斯皮尔曼相关性

f, ax = plt.subplots(figsize = (20, 6))
plt.subplot(1,2,1)
plt.title('The spearman coleration between total purchase and each weekday')
sns.heatmap(feature[[x for x in feature.columns if x not in ['total_redeem_amt', 'date'] ]].corr('spearman'),linewidths = 0.1, vmax = 0.2, vmin=-0.2)
plt.subplot(1,2,2)
plt.title('The spearman coleration between total redeem and each weekday')
sns.heatmap(feature[[x for x in feature.columns if x not in ['total_purchase_amt', 'date'] ]].corr('spearman'),linewidths = 0.1,  vmax = 0.2, vmin=-0.2)
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x27ba05e2508>
In [23]:
# 测试翌日特征与标签的独立性 Ref: https://github.com/ChuanyuXue/MVTest

# pip install mvtest
from mvtpy.mvtest import mvtest
mv = mvtest()
mv.test(total_balance_1['total_purchase_amt'], total_balance_1['weekday'])
Out[23]:
{'Tn': 6.75, 'p-value': [0, 0.01]}

4. 月份特征分析

In [24]:
# 画出每个月的购买总量分布估计图(kdeplot)

plt.figure(figsize=(15,10))
plt.title('The Probability Density of total purchase amount in Each Month')
plt.ylabel('Probability')
plt.xlabel('Amount')
for i in range(7, 12):
    sns.kdeplot(total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,i,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,i+1,1)))]['total_purchase_amt'],label='13年'+str(i)+'月')
for i in range(1, 9):
    sns.kdeplot(total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,i,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_purchase_amt'],label='14年'+str(i)+'月')
In [25]:
# 画出每个月的赎回总量分布估计图(kdeplot)

plt.figure(figsize=(15,10))
plt.title('The Probability Density of total redeem amount in Each Month')
plt.ylabel('Probability')
plt.xlabel('Amount')
for i in range(7, 12):
    sns.kdeplot(total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,i,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,i+1,1)))]['total_redeem_amt'],label='13年'+str(i)+'月')
for i in range(1, 9):
    sns.kdeplot(total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,i,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_redeem_amt'],label='14年'+str(i)+'月')
In [26]:
# 画出14年五六七八月份的分布估计图

plt.figure(figsize=(12,10))

ax = plt.subplot(2,1,1)
plt.title('The Probability Density of total purchase and redeem amount from May.14 to August.14')
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_2['total_purchase_amt'],label='八月')
ax = sns.kdeplot(total_balance_3['total_purchase_amt'],label='七月')
ax = sns.kdeplot(total_balance_4['total_purchase_amt'],label='六月')
ax = sns.kdeplot(total_balance_5['total_purchase_amt'],color='Black',label='五月')
ax = plt.subplot(2,1,2)
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_2['total_redeem_amt'],label='八月')
ax = sns.kdeplot(total_balance_3['total_redeem_amt'],label='七月')
ax = sns.kdeplot(total_balance_4['total_redeem_amt'],label='六月')
ax = sns.kdeplot(total_balance_5['total_redeem_amt'],color='Black',label='五月')
In [27]:
# 画出13年八月到九月份的分布估计图

total_balance_last_7 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,7,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,8,1)))]
total_balance_last_8 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,8,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,9,1)))]
total_balance_last_9 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,9,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,10,1)))]
total_balance_last_10 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,10,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,11,1)))]
plt.figure(figsize=(12,10))
ax = plt.subplot(2,1,1)
plt.title('The Probability Density of total purchase and redeem amount from Aug.13 to Sep.13')
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_8['total_purchase_amt'],label='August')
ax = sns.kdeplot(total_balance_last_7['total_purchase_amt'],label='July')
ax = sns.kdeplot(total_balance_last_9['total_purchase_amt'],color='Red',label='September')

ax = plt.subplot(2,1,2)
plt.ylabel('Probability')
plt.xlabel('Amount')
ax = sns.kdeplot(total_balance_last_8['total_redeem_amt'],label='August')
ax = sns.kdeplot(total_balance_last_7['total_redeem_amt'],label='July')
ax = sns.kdeplot(total_balance_last_9['total_redeem_amt'],color='Red',label='September')
ax = sns.kdeplot(total_balance_last_10['total_redeem_amt'],color='Black',label='Novermber')

5. 日期特征分析

In [28]:
# 按照每天聚合数据集

day_sta = total_balance_2[['total_purchase_amt', 'total_redeem_amt', 'day']].groupby('day', as_index=False).mean()
In [29]:
# 获取聚合后每月购买分布的柱状图
plt.figure(figsize = (12,5))
ax = sns.barplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase',palette='cividis')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase', color = 'red')
ax.legend()
plt.title("The total Purchase in Aug.14")
Out[29]:
Text(0.5, 1.0, 'The total Purchase in Aug.14')
In [30]:
# 获取聚合后每月赎回分布的柱状图
plt.figure(figsize = (12,5))
ax = sns.barplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem', palette='cividis')
ax = sns.lineplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem', color = 'red')
ax.legend()
plt.title("The total Redeem in Aug.14")
Out[30]:
Text(0.5, 1.0, 'The total Redeem in Aug.14')
In [31]:
# 画出13年九月份的分布图

plt.figure(figsize=(12,10))
day_sta = total_balance_last_9[['total_purchase_amt', 'total_redeem_amt', 'day']].groupby('day', as_index=False).mean()
plt.subplot(2,1,1)
plt.title("The total Purchase in Sep.13")
ax = sns.barplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase', palette= 'cividis')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=day_sta, label='Purchase', color = 'red')
plt.subplot(2,1,2)
plt.title("The total Redeem in Sep.13")
bx = sns.barplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem', palette= 'cividis')
bx = sns.lineplot(x="day", y="total_redeem_amt", data=day_sta, label='Redeem', color = 'red')
bx.legend()
Out[31]:
<matplotlib.legend.Legend at 0x27ba0299b88>

We find that the data from last year in Sep has very limited week feature

There are some strange day in Sep:

  1. 1st day
  2. 2nd day
  3. 16th day(Purchase a lot)---Monday & 3days before MidAutumn Festirval
  4. 11th day and 25th day(Redeem a lot)---Both of Wednesday
  5. 18 19 20(Both Purchase and Redeem is very low)

image.png

In [32]:
# 画出历史所有天的热力图

test = np.zeros((max(total_balance_1['week']) - min(total_balance_1['week']) + 1, 7))
test[total_balance_1['week'] - min(total_balance_1['week']), total_balance_1['weekday']] = total_balance_1['total_purchase_amt']

f, ax = plt.subplots(figsize = (10, 4))
sns.heatmap(test,linewidths = 0.1, ax=ax)
ax.set_title("Purchase")
ax.set_xlabel('weekday')
ax.set_ylabel('week')

test = np.zeros((max(total_balance_1['week']) - min(total_balance_1['week']) + 1, 7))
test[total_balance_1['week'] - min(total_balance_1['week']), total_balance_1['weekday']] = total_balance_1['total_redeem_amt']

f, ax = plt.subplots(figsize = (10, 4))
sns.heatmap(test,linewidths = 0.1, ax=ax)
ax.set_title("Redeem")
ax.set_xlabel('weekday')
ax.set_ylabel('week')
Out[32]:
Text(70.0, 0.5, 'week')

From the heat map we find that the data of week 4 and weekday 6 is very strange, and week 12 weekday 2 either image.png

In [33]:
# 对于热力图中异常点的数据分析.1

total_balance_1[(total_balance_1['week'] == 4 + min(total_balance_1['week'])) & (total_balance_1['weekday'] == 6)]
Out[33]:
date total_purchase_amt total_redeem_amt day month year week weekday
307 2014-05-04 303087562.0 413222034.0 4 5 2014 18 6

2014-5-4 is a special day in China, It is the first workday after the Labour day! image.png

In [34]:
# 对于热力图中异常点的数据分析.2

total_balance_1[(total_balance_1['week'] == 12 + min(total_balance_1['week'])) & (total_balance_1['weekday'] == 2)]
Out[34]:
date total_purchase_amt total_redeem_amt day month year week weekday
359 2014-06-25 264663201.0 547295931.0 25 6 2014 26 2

In 2016-06-25 They Redeemed a lot but Purchase a little: image.png

image.png

6. 节假日分析

  1. The QingMing festerval (April.5 - April.7)
  2. The Labour day (May.1 - May.5)
  3. The DuanWu festeval (May.31 - June.2)
  4. The MidAutumn festeval (Sep.6 - Sep.8)

Others

  1. Mother day(May.13)
  2. Father day(June. 17)
  3. TianMao 618 sales(June 10 - June 20)
  4. Teachers' day(Sep 9)
In [35]:
# 获取节假日的数据

qingming = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,4,5))) & (total_balance['date'] < np.datetime64(datetime.date(2014,4,8)))]
labour = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,5,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,5,4)))]
duanwu = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,5,31))) & (total_balance['date'] < np.datetime64(datetime.date(2014,6,3)))]
data618 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,6,10))) & (total_balance['date'] < np.datetime64(datetime.date(2014,6,20)))]
In [36]:
# 画出节假日与平时的均值

fig = plt.figure(figsize = (12,5))
index_list = ['清明','劳动节','端午','618','平均']
label_list = [np.mean(qingming['total_purchase_amt']), np.mean(labour['total_purchase_amt']),np.mean(duanwu['total_purchase_amt']),np.mean(data618['total_purchase_amt']),np.mean(total_balance_1['total_purchase_amt'])]
plt.bar(index_list, label_list, label="Purchase")

index_list = ['清明.','劳动.','端午.','618.','平均.']
label_list = [np.mean(qingming['total_redeem_amt']), np.mean(labour['total_redeem_amt']),np.mean(duanwu['total_redeem_amt']),np.mean(data618['total_redeem_amt']),np.mean(total_balance_1['total_redeem_amt'])]
plt.bar(index_list, label_list, label="Redeem")
plt.title("The average of different holiday")
plt.ylabel("Amount")
plt.legend()
plt.show()
In [37]:
# 画出节假日购买量与其所处翌日的对比

plt.figure(figsize = (12,5))
import numpy as np
import matplotlib.pyplot as plt
size = 4
x = np.arange(size)

total_width, n = 0.8, 2    
width = total_width / n
x = x - (total_width - width) / 2

a = [176250006, 167825284, 162844282,321591063]
b = [225337516, 241859315, 225337516,307635449]

plt.bar(x, a,  width=width, label='Holiday_Purchase')
plt.bar(x + width, b, width=width, label='Normal_Purchase')
plt.xticks(x + width / 2, ('清明', '劳动节', '端午', '618'))
plt.title("节假日购买量与其所处翌日的对比")
plt.legend()
plt.show()
In [38]:
# 画出节假日赎回量与其所处翌日的对比
plt.figure(figsize = (12,5))
import numpy as np
import matplotlib.pyplot as plt
size = 4
x = np.arange(size)

total_width, n = 0.8, 2     
width = total_width / n
x = x - (total_width - width) / 2

a = [159914308, 154717620, 154366940,291016763]
b = [235439685, 240364238, 235439685,313310347]

plt.bar(x, a,  width=width, label='Holiday_Redeem')
plt.bar(x + width, b, width=width, label='Normal_Redeem')
plt.xticks(x + width / 2, ('清明', '劳动节', '端午', '618'))
plt.title("节假日赎回量与其所处翌日的对比")
plt.legend()
plt.show()

7. 节假日周围日期分析

In [39]:
# 画出清明节与周边日期的时序图

plt.figure(figsize = (12,5))
qingming_around = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,4,1))) & (total_balance['date'] < np.datetime64(datetime.date(2014,4,13)))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=qingming_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=qingming_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=qingming, ax = ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=qingming, ax=ax)
plt.title("清明节与周边日期的时序图")
ax.legend()
Out[39]:
<matplotlib.legend.Legend at 0x27bba367208>
In [40]:
# 画出劳动节与周边日期的时序图

plt.figure(figsize = (12,5))
labour_around = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,4,25))) & (total_balance['date'] < np.datetime64(datetime.date(2014,5,10)))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=labour_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=labour_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=labour, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=labour, ax=ax)
plt.title("劳动节与周边日期的时序图")
ax.legend()
Out[40]:
<matplotlib.legend.Legend at 0x27bba3f86c8>
In [41]:
# 画出端午节与周边日期的时序图
plt.figure(figsize = (12,5))
duanwu_around = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2014,5,25))) & (total_balance['date'] < np.datetime64(datetime.date(2014,6,7)))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=duanwu_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=duanwu_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=duanwu, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=duanwu, ax=ax)
plt.title("端午节与周边日期的时序图")
ax.legend()
Out[41]:
<matplotlib.legend.Legend at 0x27ba155de08>
In [42]:
# 画出中秋与周边日期的时序图
plt.figure(figsize = (12,5))
zhongqiu = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,9,19))) & (total_balance['date'] < np.datetime64(datetime.date(2013,9,22)))]
zhongqiu_around = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,9,14))) & (total_balance['date'] < np.datetime64(datetime.date(2013,9,28)))]
ax = sns.lineplot(x="date", y="total_purchase_amt", data=zhongqiu_around, label='Purchase')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=zhongqiu_around, label='Redeem', ax=ax)
ax = sns.scatterplot(x="date", y="total_purchase_amt", data=zhongqiu, ax=ax)
ax = sns.scatterplot(x="date", y="total_redeem_amt", data=zhongqiu, ax=ax)
plt.title("中秋与周边日期的时序图(in 2013)")
ax.legend()
Out[42]:
<matplotlib.legend.Legend at 0x27ba0cbd9c8>

8. 异常值分析

In [43]:
# 画出用户交易纪录的箱型图

sns.boxplot(data_balance['total_purchase_amt'])
plt.title("The abnormal value of total purchase")
Out[43]:
Text(0.5, 1.0, 'The abnormal value of total purchase')
In [44]:
# 对于购买2e8的用户的交易行为分析

data_balance[data_balance['user_id'] == 14592].sort_values(by = 'total_redeem_amt',axis = 0,ascending = False).head()
Out[44]:
user_id report_date tBalance yBalance total_purchase_amt direct_purchase_amt purchase_bal_amt purchase_bank_amt total_redeem_amt consume_amt ... category1 category2 category3 category4 date day month year week weekday
1453311 14592 20131104 99457728 0 201768328 201768328 201275171 493157 102310600 0 ... NaN NaN NaN NaN 2013-11-04 4 11 2013 45 0
1453388 14592 20140616 0 98964529 1966014 1953569 0 1953569 100930543 0 ... NaN NaN NaN NaN 2014-06-16 16 6 2014 25 0
1453227 14592 20131226 367063 98296082 17369 0 0 0 97946388 0 ... NaN NaN NaN NaN 2013-12-26 26 12 2013 52 3
1453313 14592 20131105 97458675 99457728 4899446 4899446 4899446 0 6898499 0 ... NaN NaN NaN NaN 2013-11-05 5 11 2013 45 1
1453355 14592 20140617 0 0 339679 339679 0 339679 339679 0 ... NaN NaN NaN NaN 2014-06-17 17 6 2014 25 1

5 rows × 24 columns

1311 Bought 2E Seal 1E 1312 Seal 1E 1405 Bought 0.9E 1406 Seal 1E

In [45]:
# 画出单笔交易为2e8的那天的总交易量及附近几天的交易量

plt.figure(figsize = (12,5))
e2 = total_balance[(total_balance['date'] >= np.datetime64(datetime.date(2013,11,1))) & (total_balance['date'] < np.datetime64(datetime.date(2013,11,10)))]
ax = sns.barplot(x="day", y="total_purchase_amt", data=e2, label='2E')
ax = sns.lineplot(x="day", y="total_purchase_amt", data=e2, label='2E')
plt.title("The influence of the big deal with 200 million purchasing(Red Bar)")
ax.legend()
Out[45]:
<matplotlib.legend.Legend at 0x27ba026a648>
In [46]:
# 画出每日单笔最大交易的时序图

plt.figure(figsize=(20, 6))
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).max(), label='最大购买')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).max(), label='最大赎回')
plt.title("The Biggest deal happend in each day")
Out[46]:
Text(0.5, 1.0, 'The Biggest deal happend in each day')
In [47]:
# 画出每日单笔最大交易以及总交易额的时序图

plt.figure(figsize=(25, 8))
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_PURCHASE')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).max(), label='MAX_REDEEM')
ax = sns.lineplot(x="date", y="total_purchase_amt", data=data_balance[['total_purchase_amt', 'date']].groupby('date', as_index=False).sum(), label='TOTAL_PURCHASE')
ax = sns.lineplot(x="date", y="total_redeem_amt", data=data_balance[['total_redeem_amt', 'date']].groupby('date', as_index=False).sum(), label='TOTAL_REDEEM')
In [48]:
# 画出每个月大额交易的频次直方图

plt.figure(figsize=(12, 5))
big_frequancy = data_balance[(data_balance['total_purchase_amt'] > 10000000) | (data_balance['total_redeem_amt'] > 10000000)][['month','year','user_id']].groupby(['year','month'], as_index=False).count()
big_frequancy['i'] = big_frequancy['year']  + big_frequancy['month'] / 100
ax = sns.barplot(x="i", y="user_id", data=big_frequancy)
plt.title("The frequency of super big deal(larger than 100 million) in each month")
Out[48]:
Text(0.5, 1.0, 'The frequency of super big deal(larger than 100 million) in each month')
In [49]:
# 获取大额交易的数据集

data_balance['big_purchase'] = 0
data_balance.loc[data_balance['total_purchase_amt'] > 1000000, 'big_purchase'] = 1
data_balance['big_redeem'] = 0
data_balance.loc[data_balance['total_redeem_amt'] > 1000000, 'big_redeem'] = 1
In [50]:
# 对大额交易按每天做聚合操作

big_purchase = data_balance[data_balance['big_purchase'] == 1].groupby(['date'], as_index=False)['total_purchase_amt'].sum()
small_purchase = data_balance[data_balance['big_purchase'] == 0].groupby(['date'], as_index=False)['total_purchase_amt'].sum()
big_redeem = data_balance[data_balance['big_redeem'] == 1].groupby(['date'], as_index=False)['total_redeem_amt'].sum()
small_redeem = data_balance[data_balance['big_redeem'] == 0].groupby(['date'], as_index=False)['total_redeem_amt'].sum()
In [51]:
# 画出大额交易与小额交易的时序分布图

fig = plt.figure(figsize=(20,6))
plt.plot(big_purchase['date'], big_purchase['total_purchase_amt'],label='big_purchase', color = 'red')
plt.plot(big_redeem['date'], big_redeem['total_redeem_amt'],label='big_redeem', color = 'orange')

plt.plot(small_purchase['date'], small_purchase['total_purchase_amt'],label='small_purchase', color = 'blue')
plt.plot(small_redeem['date'], small_redeem['total_redeem_amt'],label='small_redeem', color = 'dodgerblue')
plt.legend(loc='best')
plt.title("The time series of big deal of Purchase and Redeem from July.13 to Sep.14")
plt.xlabel("Time")
plt.ylabel("Amount")
plt.show()
In [52]:
# 画出大额交易与小额交易的分布估计图

plt.figure(figsize=(15,10))

plt.subplot(2,2,1)
for i in range(4, 9):
    sns.kdeplot(big_purchase[(big_purchase['date'] >= np.datetime64(datetime.date(2014,i,1))) & (big_purchase['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_purchase_amt'],label='14Y,'+str(i)+'M')
plt.title('BIG PURCHASE')

plt.subplot(2,2,2)
for i in range(4, 9):
    sns.kdeplot(small_purchase[(small_purchase['date'] >= np.datetime64(datetime.date(2014,i,1))) & (small_purchase['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_purchase_amt'],label='14Y,'+str(i)+'M')
plt.title('SMALL PURCHASE')

plt.subplot(2,2,3)
for i in range(4, 9):
    sns.kdeplot(big_redeem[(big_redeem['date'] >= np.datetime64(datetime.date(2014,i,1))) & (big_redeem['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_redeem_amt'],label='14Y,'+str(i)+'M')
plt.title('BIG REDEEM')

plt.subplot(2,2,4)
for i in range(4, 9):
    sns.kdeplot(small_redeem[(small_redeem['date'] >= np.datetime64(datetime.date(2014,i,1))) & (small_redeem['date'] < np.datetime64(datetime.date(2014,i+1,1)))]['total_redeem_amt'],label='14Y,'+str(i)+'M')
plt.title('SMALL REDEEM')
Out[52]:
Text(0.5, 1.0, 'SMALL REDEEM')
In [53]:
# 添加时间戳

big_purchase['weekday'] = big_purchase['date'].dt.weekday
small_purchase['weekday'] = small_purchase['date'].dt.weekday
big_redeem['weekday'] = big_redeem['date'].dt.weekday
small_redeem['weekday'] = small_redeem['date'].dt.weekday
In [54]:
# 分析大额小额的翌日分布

plt.figure(figsize=(15, 10))

ax = plt.subplot(2,2,1)
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=big_purchase[big_purchase['date'] >= np.datetime64(datetime.date(2014,4,1))])
plt.title('BIG PURCHASE')

ax = plt.subplot(2,2,2)
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=big_redeem[big_redeem['date'] >= np.datetime64(datetime.date(2014,4,1))])
plt.title('BIG REDEEM')

ax = plt.subplot(2,2,3)
ax = sns.boxplot(x="weekday", y="total_purchase_amt", data=small_purchase[small_purchase['date'] >= np.datetime64(datetime.date(2014,4,1))])
plt.title('SMALL PURCHASE')

ax = plt.subplot(2,2,4)
ax = sns.boxplot(x="weekday", y="total_redeem_amt", data=small_redeem[small_redeem['date'] >= np.datetime64(datetime.date(2014,4,1))])
plt.title('SMALL REDEEM')
Out[54]:
Text(0.5, 1.0, 'SMALL REDEEM')

9. 用户交易纪录表中其他变量相关分析

In [55]:
# 截断数据集

data_balance_1 = data_balance[data_balance['date'] > datetime.datetime(2014,4,1)]
In [56]:
# 画出用户交易纪录表中其他变量与标签的相关性图


plt.figure(figsize = (12,5))
feature = ['total_purchase_amt','total_redeem_amt', 'report_date', 'tBalance', 'yBalance', 
       'direct_purchase_amt', 'purchase_bal_amt', 'purchase_bank_amt',
        'consume_amt', 'transfer_amt', 'tftobal_amt',
       'tftocard_amt', 'share_amt']

sns.heatmap(data_balance_1[feature].corr(), linewidths = 0.1)   
plt.title("The coleration between each feature in User_Balance_Table")
Out[56]:
Text(0.5, 1, 'The coleration between each feature in User_Balance_Table')

10. 银行、支付宝利率分析

In [57]:
# 读取银行利率并添加时间戳

bank = pd.read_csv(dataset_path + "mfd_bank_shibor.csv")
bank = bank.rename(columns = {'mfd_date': 'date'})
bank_features = [x for x in bank.columns if x not in ['date']]
bank['date'] = pd.to_datetime(bank['date'], format= "%Y%m%d")
bank['day'] = bank['date'].dt.day
bank['month'] = bank['date'].dt.month
bank['year'] = bank['date'].dt.year
bank['week'] = bank['date'].dt.week
bank['weekday'] = bank['date'].dt.weekday
In [58]:
# 读取支付宝利率并添加时间戳

share = pd.read_csv(dataset_path + 'mfd_day_share_interest.csv')
share = share.rename(columns = {'mfd_date': 'date'})
share_features = [x for x in share.columns if x not in ['date']]
share['date'] = pd.to_datetime(share['date'], format= "%Y%m%d")
share['day'] = share['date'].dt.day
share['month'] = share['date'].dt.month
share['year'] = share['date'].dt.year
share['week'] = share['date'].dt.week
share['weekday'] = share['date'].dt.weekday
In [59]:
# 画出上一天银行及支付宝利率与标签的相关性图

bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration between each lastday bank rate and total purchase")
temp = pd.merge(bank[['last_date']+bank_features], total_balance, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)  
plt.subplot(1,3,3)
plt.title("The coleration between each lastday bank rate and total redeem")
temp = pd.merge(bank[['last_date']+bank_features], total_balance, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
Out[59]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbb08ef08>
In [60]:
# 画出上一星期银行及支付宝利率与标签的相关性图

bank['last_week'] = bank['week'] + 1
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration between each last week bank rate and total purchase")
temp = pd.merge(bank[['last_week','weekday']+bank_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)  
plt.subplot(1,3,3)
plt.title("The coleration between each last week bank rate and total redeem")
temp = pd.merge(bank[['last_week','weekday']+bank_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
Out[60]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbb145308>
In [61]:
# 分别画出上一星期银行及支付宝利率与大额小额数据的相关性图

bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration of Small Rate purchase")
temp = pd.merge(bank[['last_date']+bank_features], small_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)  
plt.subplot(1,3,3)
plt.title("The coleration of Small Rate redeem")
temp = pd.merge(bank[['last_date']+bank_features], small_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)  

bank['last_date'] = bank['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("The coleration of Big Rate purchase")
temp = pd.merge(bank[['last_date']+bank_features], big_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)  
plt.subplot(1,3,3)
plt.title("The coleration of Big Rate redeem")
temp = pd.merge(bank[['last_date']+bank_features], big_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+bank_features]
sns.heatmap(temp.corr(), linewidths = 0.05)
Out[61]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbbffb388>
In [62]:
# 画出银行利率的时序图

plt.figure(figsize=(15,5))
for i in bank_features:
    plt.plot(bank['date'], bank[[i]] ,label=i)
plt.legend()
plt.title("The time series of bank rate")
plt.xlabel("Time")
plt.ylabel("Rate")
Out[62]:
Text(0, 0.5, 'Rate')
In [63]:
# 画出部分银行利率与购买量的时序图

fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],'b',label="Interest_3_M")
plt.plot(bank['date'], bank['Interest_6_M'],'cyan',label="Interest_6_M")
plt.plot(bank['date'], bank['Interest_9_M'],'skyblue',label="Interest_9_M")

plt.legend()

ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],'g',label="Total purchase")

plt.legend(loc=2)
plt.title("The time series of bank rate and purchase")
plt.xlabel("Time")
plt.ylabel("Rate & Amount")
plt.show()
In [64]:
# 画出部分银行利率与赎回量的时序图

fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],'b',label="Interest_3_M")
plt.plot(bank['date'], bank['Interest_6_M'],'cyan',label="Interest_6_M")
plt.plot(bank['date'], bank['Interest_9_M'],'skyblue',label="Interest_9_M")

plt.legend()

ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],'g',label="Total redeem")

plt.legend(loc=2)
plt.title("The time series of bank rate and redeem")
plt.xlabel("Time")
plt.ylabel("Rate & Amount")

plt.show()

The information for Share rate

In [65]:
# 画出支付宝利率与标签的相关性图

share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_date']+share_features], total_balance, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)  
plt.subplot(1,3,3)
temp = pd.merge(share[['last_date']+share_features], total_balance, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbc11a288>
In [66]:
# 画出银行利率与标签的相关性图

share['last_week'] = share['week'] + 1
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_week','weekday']+share_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)  
plt.subplot(1,3,3)
temp = pd.merge(share[['last_week','weekday']+share_features], total_balance, left_on=['last_week','weekday'], right_on=['week','weekday'])[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin = 0)
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbc2054c8>
In [67]:
# 画出支付宝利率与购买量的时序图

fig,ax1 = plt.subplots(figsize=(15,5))
for i in share_features:
    plt.plot(share['date'], share[i],'b',label=i)
    break
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_purchase_amt'],'g',label="Total purchase")
plt.legend(loc=2)
plt.show()
In [68]:
# 画出支付宝利率与赎回量的时序图

fig,ax1 = plt.subplots(figsize=(15,5))
for i in share_features:
    plt.plot(share['date'], share[i],'b',label=i)
    break
plt.legend()
ax2=ax1.twinx()
plt.plot(total_balance['date'], total_balance['total_redeem_amt'],'g',label="Total redeem")
plt.legend(loc=2)
plt.show()
In [69]:
# 画出大额小额数据与支付宝利率的相关性图

share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
temp = pd.merge(share[['last_date']+share_features], small_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)  
plt.title("SMALL PURCHASE")
plt.subplot(1,3,3)
plt.title("SMALL REDEEM")
temp = pd.merge(share[['last_date']+share_features], small_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)  

share['last_date'] = share['date'] + datetime.timedelta(days=1)
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.title("BIG PURCHASE")
temp = pd.merge(share[['last_date']+share_features], big_purchase, left_on='last_date', right_on='date')[['total_purchase_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)  
plt.subplot(1,3,3)
plt.title("BIG REDEEM")
temp = pd.merge(share[['last_date']+share_features], big_redeem, left_on='last_date', right_on='date')[['total_redeem_amt']+share_features]
sns.heatmap(temp.corr(), linewidths = 0.05, vmin=0)
Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x27bbcd73448>
In [70]:
# 画出银行利率与支付宝利率的时序图

fig,ax1 = plt.subplots(figsize=(15,5))
plt.plot(bank['date'], bank['Interest_3_M'],c='g',label= 'BANK')

plt.legend()
ax2=ax1.twinx()
plt.plot(share['date'], share['mfd_daily_yield'],label='SHARE')
plt.legend(loc=2)
plt.show()

11. 小结

It seems that:

  1. The influence of share is more likely to act on Purchase
  2. The influence of bank rate is more likely to act on Redeem
  3. The influence of share rate is for short
  4. The influence of bank rate is for long

based on above analysis, we can simply find these features:

  1. the weekday
  2. is it weekend
  3. is it holidy
  4. the distance from the start of week(monday)
  5. the distance from the end of week(sunday)
  6. the distance from the holiday centre(centre of QingMing DuanWu Labour ZhongQiu)
  7. the distance from the start of month
  8. the distance from the end of month
  9. the mean/max/min value of the same week in last month
  10. the value in last day of last month